Artificial Intelligence as a mediator in Understanding the Limit: student satisfaction and attitude.
Abstract
This study presents the results of a quantitative investigation on student satisfaction and attitudes toward learning the concept of limits in higher education through an instructional intervention supported by artificial intelligence (AI). Using a psychometrically validated instrument, the study evaluated perceptions related to conceptual understanding, instructional experience, and motivation. Students reported high levels of satisfaction and expressed favorable attitudes toward the use of AI as a learning support tool. Findings suggest that AI contributes to enhancing students’ disposition, motivation, and perception of mathematical learning, positioning it as a relevant and effective resource to address the educational challenges of the 21st century.
References
Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https://doi.org/10.1007/s10639-021-10573-5
Artigue, M. (1988). Ingénierie didactique. Recherches en didactique des mathematiques, 9(3), 281– 308.
Artigue, M. (2014). Didactic Engineering in Mathematics Education. En Encyclopedia of Mathematics Education (pp. 159–162). Springer Netherlands. https://doi.org/10.1007/978-94-007-4978-8_44
Bognár, L., Ágoston, G., Bacsa-Bán, A., Fauszt, T., Gubán, G., Joós, A., Juhász, L. Z., Kocsó, E., Kovács, E., Maczó, E., Kollár, A. I. M., & Strauber, G. (2024). Re-Evaluating Components of Classical Educational Theories in AI-Enhanced Learning: An Empirical Study on Student Engagement. Education Sciences 2024, Vol. 14, 14(9). https://doi.org/10.3390/EDUCSCI14090974
Carneiro, T., Da Nobrega, R. V. M., Nepomuceno, T., Bian, G. Bin, De Albuquerque, V. H. C., & Filho, P. P. R. (2018). Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access, 6, 61677–61685. https://doi.org/10.1109/ACCESS.2018.2874767
Cornu, B. (1991). Limits. En D. Tall (Ed.), Advanced Mathematical Thinking (pp. 153–166). Kluwer.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Deci, E. L., & Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior. Intrinsic Motivation and Self-Determination in Human Behavior. https://doi.org/10.1007/978-1-4899-2271-7
Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Duval, R. (2006). A Cognitive Analysis of Problems of Comprehension in a Learning of Mathematics. Educational Studies in Mathematics, 61(1–2), 103–131. https://doi.org/10.1007/s10649-006-0400-z
Ferreira, R., Canesche, M., Jamieson, P., Neto, O. P. V., & Nacif, J. A. M. (2024). Examples and tutorials on using Google Colab and Gradio to create online interactive student‐learning modules. Computer Applications in Engineering Education, 32(4). https://doi.org/10.1002/CAE.22729
García de Blanes Sebastián, M., Sarmiento Guede, J. R., Azuara Grande, A., & Filipe, A. F. (2025). UTAUT-2 predictors and satisfaction: implications for mobile-learning adoption among university students. Education and Information Technologies, 30(3), 3201–3237. https://doi.org/10.1007/s10639-024-12927-1
García-Peña, V. R., Mora-Marcillo, A. B., & Ávila-Ramirez, J. A. (2020). Artificial intelligence in education. Dominio de las ciencias, 6(3). https://dialnet.unirioja.es/servlet/articulo?codigo=8231632
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education. Center for Curriculum Redesign.
Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001
Liang, S. (2016). Teaching the Concept of Limit by Using Conceptual Conflict Strategy and Desmos Graphing Calculator. International Journal of Research in Education and Science, 2(1), 35.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.
Ludwig, L. (2024, julio 9). Lessons learned from a semester exploring AI. Mathematical Association of America. https://maa.org/math-values/lessons-learned-from-a-semester-exploring-ai/
Ma, X., & Kishor, N. (1997). Assessing the Relationship between Attitude toward Mathematics and Achievement in Mathematics: A Meta-Analysis. Journal for Research in Mathematics Education, 28(1), 26. https://doi.org/10.2307/749662
Office of Communications, C. of E. (2024, octubre 24). AI in Schools: Pros and Cons. University of Illinois. https://education.illinois.edu/about/news-events/news/article/2024/10/24/ai-in-schools--pros-and-cons
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33
Richardson, J. T. E., Slater, J. B., & Wilson, J. (2007). The National Student Survey: development, findings and implications. Studies in Higher Education, 32(5), 557–580. https://doi.org/10.1080/03075070701573757
Rojas Maldonado, E. R. (2015). El método de fermat para la enseñanza de limites. Un widget en mathematica. Revista Iberoamericana de Producción Académica y Gestión Educativa, 2(3), 1–17. https://www.pag.org.mx/index.php/PAG/article/view/525/563
Rojas Maldonado, E. R. (2016). Resultados de la aplicación de secuencias didácticas para la comprensión del concepto del límite en el bachillerato Nicolaíta. RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 6(12), 431. https://doi.org/10.23913/ride.v6i12.227
Rojas Maldonado, E. R. (2018). Mathematization: A teaching strategy to improve the learning of Calculus / Matematización: una estrategia de enseñanza para mejorar el aprendizaje del cálculo. RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 9(17). https://doi.org/10.23913/ride.v9i17.381
Rojas Maldonado, E. R. (2020). Understanding Fundamental Concepts of Calculus Through Desmos. An Intervention. RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 10(20), 1–15. https://doi.org/10.23913/ride.v10i20.672
Rojas Maldonado, E. R. (2025a). Informe estadístico de la intervención en Límites con IA. https://doi.org/10.13140/RG.2.2.27675.32806
Rojas Maldonado, E. R. (2025b). Personalización del Aprendizaje del Concepto de Límite mediante Redes Neuronales y Visualización Interactiva en Google Colab. Revista Dilemas Contemporáneos: Educación, Política y Valores, XII(3), 1–18. https://doi.org/10.46377/dilemas.v12i3.4600
Rojas Maldonado, E. R., & Toscano Galeana, J. (2025). Instrumento para la evaluación de la satisfacción del aprendizaje: diseño, validación y análisis psicométrico de una escala likert. Revista Dilemas Contemporáneos: Educación, Política y Valores., 13(1). https://doi.org/10.46377/dilemas.v13i1.4743
Rojas-Sandoval, Y., Padilla Escorcia, I., Consuegra Pacheco, S. D., Trujillo Quiroz, A., Ortega Tovar, T., Rivera Marenco, L. Y., & Rocha-Fontalvo, Y. (2021). Obstáculos epistemológicos y cognitivos en el aprendizaje del cálculo diferencial: Una mirada sobre los casos de límites especiales. Revista Boletín Redipe, 10(6), 229–244. https://doi.org/10.36260/rbr.v10i6.1321
Sahara, S., Turmudi, T., Hendriyanto, A., Muhaimin, L. H., & Mustofa, B. (2024). Interactive-visualized Tools as Means to Support Learning Calculus with Infinitesimals: A Study of the Learning Trajectory. AIP Conference Proceedings, 3220(1). https://doi.org/10.1063/5.0234975
Schunk, D. H. (2012). Learning Theories: An Educational Perspective. Pearson.
Sierpinska, A. (1985). Obstacles epistemologiques relatifs à la notion de limite. Recherches en Didactique des Mathématiques, 6(1), 5–67.
Sitzmann, T., Ely, K., Brown, K., & Bauer, K. (2010). Self-regulation theory and learning in the workplace: Long-term effects of a computer-based training intervention. Personnel Psychology, 63(2), 267–298.
Tall, D. (1991). Advanced Mathematical Thinking. En D. Tall (Ed.), Handbook of research on mathematics teaching and learning. Springer Netherlands. https://doi.org/10.1007/0-306-47203-1
Tall, D. (1995). Cognitive growth in elementary and advanced mathematical thinking. PME conference, 1(July), 1–61.
Thompson, P. W. (1994). Images of rate and operational understanding of the fundamental theorem of calculus. Educational Studies in Mathematics, 26(2–3), 229–274. https://doi.org/10.1007/BF01273664
Torres-Peña, R. C., Peña-González, D., Chacuto-López, E., Ariza, E. A., & Vergara, D. (2024). Updating Calculus Teaching with AI: A Classroom Experience. Education Sciences 2024, Vol. 14, 14(9). https://doi.org/10.3390/EDUCSCI14091019
Wu, J., Zhang, Y., & Liu, Y. (2025). Research on the application of AI in the project-based teaching design of calculus. Advances in Vocational and Technical Education, 7(2). https://doi.org/10.23977/avte.2025.070218
